evaluating predictive analytics for capacity planning · predictive analytics is the practice of...
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Evaluating Predictive Analytics for Capacity Planning
HIC 2015Andrae Gaeth
Predictive analytics is the practice of extracting information from existing data sets, and then applying various techniques (eg, statistical, modelling) in order to determine patterns and predict future outcomes and trends.
How can we practically evaluate and use predictive analytics solutions for capacity planning within health?
What is predictive analytics?
Paradigm Shift Required for Analytics Maturity
Sources:
Competing on Analytics, Tom Davenport
Gartner IT Glossary
What happened?
What will happen?
What should I do?
Why did it happen?
What is happening?
Descriptive
Predictive
Prescriptive
Real Time
Descriptive
What to look for with Capacity Planning
1. Manages multiple planning horizons
– Multi-year, annual, 6-8 week scheduling periods, weeks, days and hours
2. Continuously updates forecasts
3. Forecasts patient demand with consistently high absolute accuracy vs. stated accuracy % aggregated over time
4. Forecasts volumes for door-to-door patient flow vs. preset intervals and departments
5. Converts forecasts automatically into capacity and staffing needs
6. Supports user insight, input and adjustment as part of the planning process
Strategic & Annual Planning
• Discuss “what-if” options, regional plan
• Budget and physical capacity decisions
• Set targets and assumption (linking plans)
Monthly & Weekly Planning
• Manage “current” variation to plan
• Update forecasts & roster staff
• Informed decision making
Daily Planning
• Unit focus: manage current & projected pts.
• Focus on relieving immediate flow issues
• Replace sick leave? Book casual
Forecasting Methods Used for Operational Planning
Predictive modeling
Algorithmic modeling
Pattern identification
Scenario modeling
Simulation
Optimization
Predictive
Determining, mathematically, the relationship between the explanatoryvariables and the predicted variable, based on historical data
Examples:
Insurance: relationship between certain characteristics of a person and lifestyle and predicted outcome of a certain claim
Capacity Planning: relationship between status of ED at a given time of day and the impact on Inpatient beds tomorrow
Pattern Identification
‘Time series forecasting’ identifies different patterns within the predicted variable itself, such as trend, seasonality and day of the week. Various combinations of those patterns can then be used to derive a forecast
AlgorithmicTake recent averages and distributions of past activity for certain locations, services and day of the week or special event days. Then apply an algorithm to utilize these to create a projection
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145,201 150,721 151,870 28,384 5.6 5.3 5.0
122,548 130,929 133,984 23,016 6.1 5.7 5.3
22,653 19,792 17,887 5,368 4.1 3.7 3.5
4,229 4,984 5,546 764 7.6 6.5 7.0
4,113 4,509 5,304 727 7.6 6.2 7.0
115 475 242 37 6.8 12.8 7.8
2,732 2,764 2,536 1,156 2.4 2.4 2.2
1,510 1,630 1,506 615 3.0 2.6 2.5
1,222 1,134 1,030 541 2.0 2.1 1.8
14,197 14,147 14,058 3,488 4.3 4.1 4.0
9,128 10,111 10,041 2,314 4.6 4.4 4.3
5,068 4,036 4,017 1,174 3.9 3.4 3.3
43,161 43,830 41,924 6,800 6.5 6.4 6.0
31,140 33,538 32,961 4,549 7.3 7.4 6.7
12,021 10,292 8,963 2,251 5.0 4.6 4.4
Emergency 4,265 4,884
Scheduled 2,410 2,059
IP Surgery 6,675 6,943
Emergency 1,968 2,354
Scheduled 1,302 1,200
IP Cardiac 3,270 3,554
Emergency 543 756
Scheduled 17 31
ICU 560 787
Emergency 511 606
Scheduled 614 564
Cardiac ICU 1,125 1,170
25,266
Scheduled 5,555 5,042
Annual Patient Turn
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Hospital Total 25,756 30,308
Emergency 20,201
Annual Census Annual Patients In
Scenario
Bed days 4% Activity 16% ALOS 12%
• Analyze which services were driving these trends?
• What do we expect this year? Sustain or continue trends?
• What change initiatives are we investing in to increase volumes and
reduce ALOS?
Allow users to input their own assumptions or ‘what ifs’ into the prediction to assess impacts (vs. based purely on patterns of past activity)
SimulationCoded logic which emulates the actual individual patient ADT activity of planned patient admissions/bookings
Using either a mathematical or algorithmic approach to derive the optimal
outcome, based on an ‘objective function’.
‘Business as usual’ would have
required 50 – 60 beds staffed;
using an optimised activity plan
resulted in 40 - 50
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Optimisation
Patient pathways leveraged to simulate and/or optimise any impact on OR’s and inpatient units.
Pathways
Develops base patient demand based on current trends seen from historical data.
Historical Data
Algorithms applied to forecast increasing accuracy of patient demand.
Algorithms
Combining Approaches
Customers clinical expert input of areas that may impact any patient demand added.
Clinical Input
Aggregate vs. Patient-level
Static vs. Dynamic
Short term vs multi-horizon
Top down vs. Bottom up
Comparing Planning Approaches